Accelerating Prostate Diffusion-weighted MRI Using a Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study

Abstract

Purpose: To investigate the feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN).

Materials and methods: Raw data from the prostate DWI scans were retrospectively gathered between July 2018 and July 2019 from six single-vendor MRI scanners. There were 103 datasets used for training (median age, 64 years; interquartile range [IQR], 11), 15 for validation (median age, 68 years; IQR, 12), and 37 for testing (median age, 64 years; IQR, 12). High b-value diffusion-weighted (hb DW) data were reconstructed into noisy images using two averages and reference images using all 16 averages. A conventional DnCNN was modified into a guided DnCNN, which uses the low b-value DW image as a guidance input. Quantitative and qualitative reader evaluations were performed on the denoised hb DW images. A cumulative link mixed regression model was used to compare the readers' scores. The agreement between the apparent diffusion coefficient (ADC) maps (denoised vs reference) was analyzed using Bland-Altman analysis.

Results: Compared with the original DnCNN, the guided DnCNN produced denoised hb DW images with higher peak signal-to-noise ratio (32.79 ± 3.64 [standard deviation] vs 33.74 ± 3.64), higher structural similarity index (0.92 ± 0.05 vs 0.93 ± 0.04), and lower normalized mean square error (3.9% ± 10 vs 1.6% ± 1.5) (P < .001 for all). Compared with the reference images, the denoised images received higher image quality scores from the readers (P < .0001). The ADC values based on the denoised hb DW images were in good agreement with the reference ADC values (mean ADC difference ranged from -0.04 to 0.02 × 10^-3 mm²/sec).

Conclusion: Accelerating prostate DWI by reducing the number of acquired averages and denoising the resulting image using the proposed guided DnCNN is technically feasible. Supplemental material is available for this article. © RSNA, 2020.

Figure 1:

Schematic flow of image preprocessing steps (details are in Appendix E3 [supplement]). Raw k-space data from one diffusion-weighted (DW) scan is reconstructed to produce three types of images: guidance (low b-value DW image reconstructed with all available averages), noisy (high b-value DW image reconstructed using two averages), and reference (high b-value DW image, reconstructed using 16 averages).

Figure 2:

Denoising convolutional neural network (DnCNN) design. Guidance image is only used in the guided DnCNN. Residual image is estimation of noise, which is subtracted from the noisy image to produce the denoised image. Mean squared error (MSE), calculated between the denoised image and the reference image, is used as loss function. BN = batch normalization, Conv = convolutional, ReLU = rectified linear units.

Figure 3:

Denoising using denoising convolutional neural network (DnCNN) and guided DnCNNs. A, B, Two separate slices from the same patient. C, D, Zoomed-in view of the boxed regions in A and B. Low b-value images were used in guided DnCNN only. The reference image is a high b-value diffusion-weighted (DW) image reconstructed using 16 averages corresponding to an acquisition time of 376 seconds. Noisy image is a high b-value DW image reconstructed using two averages corresponding to an acquisition time of 47 seconds. DnCNN and guided DnCNN correspond to a noisy image denoised using either original DnCNN or guided DnCNN. Anatomic structures are better visualized on guided DnCCN images compared with DnCNN images; for example, the right hip joint (arrows in A and B), rectum (arrowheads in B), junction between the peripheral zone and transition zone (dashed arrows in C), and bladder wall (* in D). White dashed line in the noisy image in A shows the location of the intensity profiles plotted in Figure 4. Acquisition times are proportional to the repetition time of 7833 msec, three diffusion directions, and corresponding number of averages.

Figure 4:

Signal intensity profiles from the reference, denoised with guided denoising convolutional neural network, and noisy high b-value diffusion-weighted images shown in Figure 3, A. Denoising reduces the peak-to-peak noise fluctuations and noise-induced signal bias. AU = arbitrary units.

Figure 5:

Representative examples of diffusion-weighted (DW) images and derived apparent diffusion coefficient (ADC) maps. A and B show images from two separate patients (not used in Figure 3). DW image column contains low b-value image (four averages), noisy and/or denoised (two averages), and reference (16 averages) high b-value DW images. Low b-value image was used to compute the ADC maps. Noisy ADC maps strongly underestimate ADC values. Denoised ADC maps with an acquisition time of 48 seconds are comparable with the reference ADC maps with an acquisition time of 160 seconds. Zoomed-in view on the boxed regions from A and B are displayed in C and D. Acquisition times are based on a repetition time of 8000 msec, single diffusion direction, and corresponding number of averages.

Figure 6:

Bland-Altman plots show per-patient analysis between apparent diffusion coefficient (ADC) values measured using the reference ADC map and the denoised ADC map in, A, muscle, B, peripheral zone, C, transition zone, and, D, cancer lesion. The solid line represents the mean difference (bias), and the dotted lines represent the 95% limits of agreement.